For over two years, the largest open code dataset was The Stack v2… until today. 🥞 The Stack v3 is out: the largest open code dataset ever released: 114 TB, 770 languages, 224M repositories, ~5T tokens of deduplicated and filtered source code. Fully open, no restrictively licensed code included. The upgrade: - v2 (2024): 68 TB raw -> 2 TB / ~550B tokens, 618 languages filtered - v3 (2026): 114 TB raw -> 15.9 TB / ~5T tokens, 713 languages filtered C++ x15, TypeScript x7.5, Rust x7, Python x4.8. Even the COBOL corner of GitHub got a bigger slice. Part of that is two fresh years of open source. Part of it is a bug we found in v2's deduplication - story below. 👀 Things that make v3 different: 1. Contents inline. The #1# complaint about v2 was "cool dataset, where's the actual code?" v2 shipped file IDs from the Software Heritage graph, and fetching contents was a DIY treasure hunt. v3 is self-contained: sources embedded directly, one row = one repository. Download finishes -> you start training. 2. Fresh crawl. v2 was a 2023 snapshot of even older crawls. v3 is a direct re-crawl of GitHub at the latest commit, completed by August 2025: 224M repositories, 44B files. Forks were only included if they had 5+ stars. Two ways in: 🥞 stack-v3-train - near-deduplicated, quality-filtered, PII-redacted, contents inline. Point load_dataset at it and go. 🏗 stack-v3-full - the entire 114 TB corpus as an HF Storage Bucket: every duplicate kept with cluster IDs, stubs for excluded files. Roll your own dedup, filters, and mixes. The record isn't really ours to claim - it's millions of everyone's repositories, neatly stacked. Thanks to every developer who keeps their code public, the BigCode community, and friends who built the infrastructure that made this possible!

OpenAI should release a detailed transcript from the Hugging Face hacking incident -- it would be helpful for the field learn from. Did the top-level agent know about the hacking, or was there some "value drift" between it and its subagents? How did it rationalize its behavior?

vLLM Office Hours today at 2pm ET: RL at 1T Scale, a prime-rl performance deep dive with @m_sirovatka (@PrimeIntellect). Training trillion-parameter MoE models like GLM-5.1 on agentic RL, plus what's new in vLLM 0.25 by @mgoin_. Get a recurring cal invite:

Today we are announcing a partnership with the Department of Energy to build Genesis-Science-1, an open model for scientific research. GS1 is an American open-weight AI model and governed research harness designed to complete scientific computing workflows while preserving a reproducible record of its work. This model will be shaped by the people who know scientific work inside and out. @ENERGY is opening a contributor program for researchers, laboratories, universities, companies, and nonprofits, and we're speaking with infrastructure partners who can add training or evaluation capacity. There’s still lots of work ahead, and we hope you’ll help us build in the open, starting with GS1.

We're partnering with @huggingface to investigate an unprecedented security incident. Cyber-capable OpenAI models compromised Hugging Face production during a benchmark evaluation. Sharing preliminary findings to help defenders understand emerging risks:

Today we are releasing Laguna S 2.1. At 118B total parameters, with 8B active per token, it does the work of models several times its size on agentic coding. It is remarkably persistent across long-horizon tasks. And it is small enough to run on a single NVIDIA DGX Spark. It is far more capable than anything we have created before, and I think it redefines what a model in its weight class can do. Laguna S 2.1 is an important model for Poolside. What it represents is even more important. If, five years ago, I had read a book that said that by 2030 everything economically valuable, scientifically interesting, and personally meaningful would be built on intelligence contracted from three or four companies, I would have called it dystopian science fiction. We are at a fork in the road of what kind of world we can have. I believe intelligence should and will become a commodity. The question is whether that intelligence comes from three companies, or from many people who can build it, own it, and shape it. The open ecosystem will not win by being the best in its own category. No one cares who is king of the open-source kingdom. People want the best intelligence for the task they are trying to do, with the right balance of quality, speed, cost, and control. If we want a different future, open models have to be on par with, or better than, their closed equivalents. Laguna S 2.1 is a meaningful step in that direction: capable enough to compete far above its weight class, efficient enough to run on hardware you can own, and open-weight so anyone can build on it. Open-weighting our models is the contribution we can make today toward a world where intelligence can be built and owned by many. And we will keep doing it. I am very proud of this team’s work. A big shout out to everyone at Poolside who made this possible, from infrastructure and data to architecture, pretraining, post-training, evaluations, and inference. Laguna S 2.1 is available today under the OpenMDW-1.1 license, with weights on Hugging Face and access through OpenRouter and our API. We are building toward a future where the most capable intelligence in the world can be owned and shaped by anyone. Laguna S 2.1 is one step. We are going to keep building until that future exists.